Land-cover change detection using paired OpenStreetMap data and optical high-resolution imagery via object-guided Transformer

Optical high-resolution imagery and OpenStreetMap (OSM) data are two important data sources for land-cover change detection. Previous studies in these two data sources focus on utilizing the information in OSM data to aid the change detection on multi-temporal optical high-resolution images. This paper pioneers the direct detection of land-cover changes utilizing paired OSM data and optical imagery, thereby broadening the horizons of change detection tasks to encompass more dynamic earth observations. To this end, we propose an object-guided Transformer (ObjFormer) architecture by naturally combining the prevalent object-based image analysis (OBIA) technique with the advanced vision Transformer architecture. The introduction of OBIA can significantly reduce the computational overhead and memory burden in the self-attention module. Specifically, the proposed ObjFormer has a hierarchical pseudo-siamese encoder consisting of object-guided self-attention modules that extract representative features of different levels from OSM data and optical images; a decoder consisting of object-guided cross-attention modules can progressively recover the land-cover changes from the extracted heterogeneous features. In addition to the basic supervised binary change detection task, this paper raises a new semi-supervised semantic change detection task that does not require any manually annotated land-cover labels of optical images to train semantic change detectors. Two lightweight semantic decoders are added to ObjFormer to accomplish this task efficiently. A converse cross-entropy loss is designed to fully utilize the negative samples, thereby contributing to the great performance improvement in this task. The first large-scale benchmark dataset containing 1,287 map-image pairs (1024$\times$ 1024 pixels for each sample) covering 40 regions on six continents ...(see the manuscript for the full abstract

High Density HEK293T culture for high yield, high quality, stable adenoviral vector production in Ambr® 250 stirred tank reactors

Adenovirus vectors (AdV) present high safety and immunogenicity for drug development, allowing more and more vaccines to adopt this technology platform in recent years. Due to the current COVID-19 pandemic, the global demand for AdV has experienced significant growth. Therefore, to optimize the upstream process in order to obtain high yields, good quality and stable viral vectors, it becomes critical that processes are stable and easy to scale-up, which has become a key focus of pharmaceutical companies in the field. Please click Download on the upper right corner to see the full abstract

Gender-specific effects of oxidative balance score on the prevalence of diabetes in the US population from NHANES

BackgroundThe relationship between oxidative balance score (OBS) and diabetes remains poorly understood and may be gender-specific. We conducted a cross-sectional study to investigate the complex association between OBS and diabetes among US adults.MethodsOverall, 5,233 participants were included in this cross-sectional study. The exposure variable was OBS, composed of scores for 20 dietary and lifestyle factors. Multivariable logistic regression, subgroup analysis, and restricted cubic spline (RCS) regression were applied to examine the relationship between OBS and diabetes.ResultsCompared to the lowest OBS quartile group (Q1), the multivariable-adjusted odds ratio (OR) (95% confidence interval (CI) for the highest OBS quartile group (Q4) was 0.602 (0.372–0.974) (p for trend = 0.007), and for the highest lifestyle, the OBS quartile group was 0.386 (0.223–0.667) (p for trend < 0.001). Moreover, gender effects were found between OBS and diabetes (p for interaction = 0.044). RCS showed an inverted-U relationship between OBS and diabetes in women (p for non-linear = 6e−04) and a linear relationship between OBS and diabetes in men.ConclusionsIn summary, high OBS was negatively associated with diabetes risk in a gender-dependent manner

Curcumin Reduces Cognitive Deficits by Inhibiting Neuroinflammation through the Endoplasmic Reticulum Stress Pathway in Apolipoprotein E4 Transgenic Mice.

Apolipoprotein E4 (ApoE4) is the main genetic risk factor for Alzheimer's disease (AD), but the exact way in which it causes AD remains unclear. Curcumin is considered to have good therapeutic potential for AD, but its mechanism has not been clarified. This study aims to observe the effect of curcumin on ApoE4 transgenic mice and explore its possible molecular mechanism. Eight-month-old ApoE4 transgenic mice were intraperitoneally injected with curcumin for 3 weeks, and the Morris water maze test was used to evaluate the cognitive ability of the mice. Immunofluorescence staining, immunohistochemistry, western blotting, and enzyme-linked immunosorbent assay (ELISA) were used to examine the brain tissues of the mice. Curcumin reduced the high expression of ApoE4 and the excessive release of inflammatory factors in ApoE4 mice. In particular, the expression of marker proteins of endoplasmic reticulum (ER) stress was significantly increased in ApoE4 mice, while curcumin significantly reduced the increase in the expression of these proteins. Collectively, curcumin alleviates neuroinflammation in the brains of ApoE4 mice by inhibiting ER stress, thus improving the learning and cognitive ability of transgenic mice

Dual functions of the ZmCCT-associated quantitative trait locus in flowering and stress responses under long-day conditions

Gene ontology enrichment of differentially expressed genes in HZ4 and HZ4-NIL in three development stages. (XLS 21 kb

Soluble CD83 Alleviates Experimental Autoimmune Uveitis by Inhibiting Filamentous Actin-Dependent Calcium Release in Dendritic Cells

Soluble CD83 (sCD83) is the extracellular domain of the membrane-bound CD83 molecule, and known for its immunoregulatory functions. Whether and how sCD83 participates in the pathogenesis of uveitis, a serious inflammatory disease of the eye that can cause visual disability and blindness, is unknown. By flow cytometry and imaging studies, we show that sCD83 alleviates experimental autoimmune uveitis (EAU) through a novel mechanism. During onset and recovery of EAU, the level of sCD83 rises in the serum and aqueous humor, and CD83+ leukocytes infiltrate the inflamed eye. Systemic or topical application of sCD83 exerts a protective effect by decreasing inflammatory cytokine expression, reducing ocular and splenic leukocyte including CD4+ T cells and dendritic cells (DCs). Mechanistically, sCD83 induces tolerogenic DCs by decreasing the synaptic expression of co-stimulatory molecules and hampering the calcium response in DCs. These changes are caused by a disruption of the cytoskeletal rearrangements at the DC–T cell contact zone, leading to altered localization of calcium microdomains and suppressed T-cell activation. Thus, the ability of sCD83 to modulate DC-mediated inflammation in the eye could be harnessed to develop new immunosuppressive therapeutics for autoimmune uveitis

Analysis of China's carbon market price fluctuation and international carbon credit financing mechanism using random forest model.

This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model's Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism